TY - GEN
T1 - Combining powers of two predictors in optimizing real-time bidding strategy under constrained budget
AU - Lin, Chi Chun
AU - Chuang, Kun Ta
AU - Wu, Wush Chi Hsuan
AU - Chen, Ming Syan
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - We address the bidding strategy design problem faced by a Demand-Side Platform (DSP) in Real-Time Bidding (RTB) advertising. A RTB campaign consists of various parameters and usually a predefined budget. Under the budget constraint of a campaign, designing an optimal strategy for bidding on each impression to acquire as many clicks as possible is a main job of a DSP. State-of-the-art bidding algorithms rely on a single predictor, namely the clickthrough rate (CTR) predictor, to calculate the bidding value for each impression. This provides reasonable performance if the predictor has appropriate accuracy in predicting the probability of user clicking. However when the predictor gives only moderate accuracy, classical algorithms fail to capture optimal results. We improve the situation by accomplishing an additional winning price predictor in the bidding process. In this paper, a method combining powers of two prediction models is proposed, and experiments with real world RTB datasets from benchmarking the new algorithm with a classic CTR-only method are presented. The proposed algorithm performs better with regard to both number of clicks achieved and effective cost per click in many different settings of budget constraints.
AB - We address the bidding strategy design problem faced by a Demand-Side Platform (DSP) in Real-Time Bidding (RTB) advertising. A RTB campaign consists of various parameters and usually a predefined budget. Under the budget constraint of a campaign, designing an optimal strategy for bidding on each impression to acquire as many clicks as possible is a main job of a DSP. State-of-the-art bidding algorithms rely on a single predictor, namely the clickthrough rate (CTR) predictor, to calculate the bidding value for each impression. This provides reasonable performance if the predictor has appropriate accuracy in predicting the probability of user clicking. However when the predictor gives only moderate accuracy, classical algorithms fail to capture optimal results. We improve the situation by accomplishing an additional winning price predictor in the bidding process. In this paper, a method combining powers of two prediction models is proposed, and experiments with real world RTB datasets from benchmarking the new algorithm with a classic CTR-only method are presented. The proposed algorithm performs better with regard to both number of clicks achieved and effective cost per click in many different settings of budget constraints.
UR - http://www.scopus.com/inward/record.url?scp=84996602244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996602244&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983656
DO - 10.1145/2983323.2983656
M3 - Conference contribution
AN - SCOPUS:84996602244
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2143
EP - 2148
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Y2 - 24 October 2016 through 28 October 2016
ER -